Preparing Data for Digital Transformation

By Nick Parkin

Data is one of the primary assets of almost any business today, but there’s rarely a value given to an organisation’s data.

When it comes time to migrate your data to a new system as part of your digital transformation efforts, you can’t just take what you have and move it over. The lift and shift approach isn’t efficient and may, in the long run, cost more and produce less.

Rather, you must prepare your data for digital transformation. This requires taking a close inventory of what you have, what you need to keep and what you can get rid of.

Only when you’ve done this is your data really going to be ready for digital transformation.

A massive proliferation of documents and data 

Today’s organisations have massive volumes of data, some of it structured, but much of it is unstructured. And it’s continuing to proliferate. Imagine if data were like objects in your house. Let’s say you’ve lived in this house for years, and every room is filled to overflowing. Maybe the house is so full that all you can manage is to get the front door open, climb the staircase and throw yourself on the bed. It’s just that full. Now, what if your landlord sells the house and suddenly you have to move to a one-bedroom apartment. You certainly can’t take decades’ worth of stuff with you; you’re going to have to do some serious cleaning and ensure you only take with you the things you need.

This is the current state of affairs for organisations with huge systems that are trying to digitally transform in 2023; they’re trying to move, but they’ve got all this data “stuff.” That is a real problem, because they’ve got no idea what’s in the “house” unless they go through it. But they haven’t got time to do this, and they’ve got no idea what to throw away or what’s valuable.

This is the major issue: what is valuable data? Is it the data that’s 20 years old? Is it the stuff that’s only a year old? Maybe for some people in the organisation, there is still value to that 20-year-old data. Or maybe it’s unnecessary and you need to clear it out.

Taking stock: What’s needed and what’s not

vital business data

Professional organising consultant Marie Kondo has won acclaim for her simple recommendation on tidying up that asks people to think about whether something still sparks joy. If it doesn’t, she instructs, you should let go of it. That’s a good metaphor for getting rid of data. So much data is being held onto by organisations, not because they really need it but because that’s how they’ve always done it. And going through all that data – maybe a hundred terabytes or more – can seem like a daunting and complex undertaking.

When the time comes, though, you’ll have IT on one side wanting to get rid of as much data as possible. You’ll have the line of business on the other side not wanting to get rid of any of their data. This creates a data tug of war. Eventually, there will be an evolution of AI-based tools that can help sort this out, but the solutions aren’t quite there yet. 

Six steps for preparing the data

How do you get started with tackling this effort?

Setting the stage: First, you’ll need to appoint the team that will take responsibility for it and develop a plan that will make the most of it. To guarantee that the time efficiencies will be maintained throughout data gathering, incorporate automated processes with strong AI underpinnings and reliable rules from the beginning. Basically, control the archiving process to guarantee that you reach data storage nirvana while being compliant.

Assess your resources and systematically combine them: Data can be replicated, readily fragmented, and exists across multiple platforms due to the use of numerous applications. As a result, it is important to aggregate the data. 

Evaluate the data in your stockpile: If there is proof that the data has value, keep it. And conduct your due diligence to ensure that what is left complies with legal and regulatory requirements. If you don’t need to keep it for those reasons, throw it away.

Check for accuracy: This is a key part of determining value. Inaccurate data can cause problems in an organization’s business processes.

Watch out for “dark data”: Dark data can exist in both structured and unstructured data; it resembles an iceberg whose top you can see but whose body you can’t because it’s below the surface. The amount of data that needs to be maintained for regulatory purposes means that this type of data can increase rapidly year after year, which presents a challenge for the CIO. More money and risk are involved in storing this kind of data than it is worth, based on the value derived from it. To get around this, initial automation and archiving efficiencies will only permit storage of the necessary data.

Conduct data analysis: To fully benefit from the potential presented by digital transformation, the organisation must not only acquire and analyse the data but also preserve it and make sure it is properly cleaned and stored.

Take the time to create value

Data is one of the primary assets of almost any business today, but there’s rarely a value given to an organisation’s data. But because of the inherent and potential value of your data, you must take time to go through it and evaluate it to truly succeed with your digital transformation. Use the steps outlined above to ensure that your data is properly prepped for migration.

This article was originally published on 28 May 2023.

About the Author

Nick ParkinNick Parkin is the CEO and Founder of Proceed Group, the expert in SAP data management, with over twenty years of experience in SAP archiving, content management and legacy systems decommissioning. Parkin established Proceed Group in 2001 to provide expert data archiving solutions for organizations struggling with increasing data volumes.


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